Inequality and the American Dream In this course we will explore sociological attempts to explain who “gets ahead” in the contemporary United States. We will discuss two distinct issues that are often conflated in public discussions: economic inequality and social mobility.  We will consider the conceptual and empirical associations between these measures, how each has changed over time, how the United States compares to other countries, and how different social environments (such as colleges, neighborhoods, and families) influence life chances within and across generations. We will also examine the challenges of producing research about these topics, focusing on both theoretical and methodological issues.

The Logic of Sociological Inquiry In this course students will be introduced to the basic tools of sociological research including problem formulation, strategies of design and data collection, and analysis and presentation of results. This class will help students formulate a research question and develop a research strategy to best explore that question. Those strategies may include interviews, structured observation, participant observation, content analysis, and surveys. This class, strongly recommended for juniors, will culminate in the submission of a senior project proposal. (SOAN 0105 or SOCI 0105)

Higher Education and Society Concerns about quality, value, and cost have raised doubts about whether higher education remains a pathway to opportunity. In this course we will consider these issues by reviewing research on the changing demographics of students, the evolving definition of “merit” in admissions, the challenges of assessing what students learn, and the relationship between student loan debt and economic inequality. We will also examine how college shapes later outcomes such as income, health, and family formation. Finally, we will discuss efforts to reform higher education and the potential for innovations like global expansion to reshape postsecondary schooling in the future. 3 hrs.lct.

Social Statistics In this course we will learn the practical tools social sociologists and other scientists use to analyze data quantitatively. Topics will emphasize applications with statistical software and data from the General Social Survey and other datasets. We will explore methods to describe statistics about samples, apply the principles of probability to make predictions about populations, and estimate the significance of those predictions through inference and hypothesis testing. We will conclude with an introduction to linear regression. (Open only to majors or by Instructor Approval)

Topics in Sociological Practice In this project-based course, we will use tools of data analysis and digital methods to explore sociological research topics and communicate findings. We will review examples of public social science such as the Opportunity Atlas, Social Explorer, and the Open Policing Project. Through readings and workshops, we will reflect on how these forms create challenges and opportunities related to data ethics, principles of data visualization, and digital pedagogy. Students will build on their previous coursework to develop interactive websites that can be integrated throughout the department's curricula. Students will extensively use programs such as R and Datawrapper but no previous experience with these tools is required. (Open to Sociology majors)

The Family in Contemporary Society This course will investigate the social, economic, and political forces that have brought about changes in family life in the beginning of the 21st century. We will begin by looking at various attempts to define "the family," and we will then explore a range of topics, including the webs of family relationships (e.g., mothering, fathering, kin networks), labor and family intersections (e.g., mediating between work and family; the household division of labor), gay and lesbian family life, and domestic violence. Although the focus will be on contemporary United States, we will also examine some cross-cultural and historical material.

Data Science Across Disciplines In this course, we will gain exposure to the entire data science pipeline—obtaining and cleaning large and messy data sets, exploring these data and creating engaging visualizations, and communicating insights from the data in a meaningful manner. During morning sessions, we will learn the tools and techniques required to explore new and exciting data sets. During afternoon sessions, students will work in small groups with one of several faculty members on domain-specific research projects in Sociology, Neuroscience, Animation, Art History, or Environmental Science. This course will utilize the R programming language. No prior experience with R is necessary. ENVS: Students will engage in research within environmental health science—the study of reciprocal relationships between human health and the environment. High-quality data and the skills to make sense of these data are key to studying environmental health across diverse spatial scales, from individual cells through human populations. In this course, we will explore common types of data and analytical tools used to answer environmental health questions and inform policy. FMMC: Students will explore how to make a series of consequential decisions about how to present data and how to make it clear, impactful, emotional or compelling. In this hands-on course we will use a wide range of new and old art making materials to craft artistic visual representations of data that educate, entertain, and persuade an audience with the fundamentals of data science as our starting point. NSCI/MATH: Students will use the tools of data science to explore quantitative approaches to understanding and visualizing neural data. The types of neural data that we will study consists of electrical activity (voltage and/or spike trains) measured from individual neurons and can be used to understand how neurons respond to and process different stimuli (e.g., visual or auditory cues). Specifically, we will use this neural data from several regions of the brain to make predictions about neuron connectivity and information flow within and across brain regions. SOCI: Students will use the tools of data science to examine how experiences in college are associated with social and economic mobility after college.  Participants will combine sources of "big data" with survey research to produce visualizations and exploratory analyses that consider the importance of higher education for shaping life chances.  HARC: Students will use the tools of data science to create interactive visualizations of the Dutch textile trade in the early eighteenth century. These visualizations will enable users to make connections between global trade patterns and representations of textiles in paintings, prints, and drawings.

Matt Lawrence

Reviews and ratings for Matt Lawrence at Middlebury College.Matt Lawrence teaches Inequality & American Dream, Soc Research Methods, Higher Education and Society and more.  Explore top rated instructors and find the best for you.Is Matt Lawrence good ? Find out on MiddCourses.

This course was super interesting for me and if you are able to actively participate, you will learn a lot of really interesting information about higher education. I would really recommend this class to anyone who wants to learn more about how higher education functions as a machine.

This course was the perfect balance of engaging material and a manageable workload. The bulk of the time commitment comes from readings due before every class. Professor Lawrence is  incredibly enthusiastic and helpful, and very good at facilitating  discussions. I would recommend this course to anyone, sociology major or not.

Professor Lawrence is such a good professor. He is very well read and engaging, welcoming of discussion and hears everyone out, and a very kind guy. The readings can be rigorous but the material is really interesting. You'll read about people's personal experiences and the data behind inequality, but the discussions Lawrence facilitates are the best part of the class. 